# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule, auto_fp16

from ..builder import NECKS
from mmcv.ops import DeformConv2dPack
from mmcv.ops.modulated_deform_conv import ModulatedDeformConv2d
import torch



@NECKS.register_module()


class FPNDcnLconv3Dcn_afa(BaseModule):
    r"""Feature Pyramid Network.

    This is an implementation of paper `Feature Pyramid Networks for Object
    Detection <https://arxiv.org/abs/1612.03144>`_.

    Args:
        in_channels (List[int]): Number of input channels per scale.
        out_channels (int): Number of output channels (used at each scale)
        num_outs (int): Number of output scales.
        start_level (int): Index of the start input backbone level used to
            build the feature pyramid. Default: 0.
        end_level (int): Index of the end input backbone level (exclusive) to
            build the feature pyramid. Default: -1, which means the last level.
        add_extra_convs (bool | str): If bool, it decides whether to add conv
            layers on top of the original feature maps. Default to False.
            If True, its actual mode is specified by `extra_convs_on_inputs`.
            If str, it specifies the source feature map of the extra convs.
            Only the following options are allowed

            - 'on_input': Last feat map of neck inputs (i.e. backbone feature).
            - 'on_lateral':  Last feature map after lateral convs.
            - 'on_output': The last output feature map after fpn convs.
        extra_convs_on_inputs (bool, deprecated): Whether to apply extra convs
            on the original feature from the backbone. If True,
            it is equivalent to `add_extra_convs='on_input'`. If False, it is
            equivalent to set `add_extra_convs='on_output'`. Default to True.
        relu_before_extra_convs (bool): Whether to apply relu before the extra
            conv. Default: False.
        no_norm_on_lateral (bool): Whether to apply norm on lateral.
            Default: False.
        conv_cfg (dict): Config dict for convolution layer. Default: None.
        norm_cfg (dict): Config dict for normalization layer. Default: None.
        act_cfg (str): Config dict for activation layer in ConvModule.
            Default: None.
        upsample_cfg (dict): Config dict for interpolate layer.
            Default: `dict(mode='nearest')`

    Example:
        >>> import torch
        >>> in_channels = [2, 3, 5, 7]
        >>> scales = [340, 170, 84, 43]
        >>> inputs = [torch.rand(1, c, s, s)
        ...           for c, s in zip(in_channels, scales)]
        >>> self = FPN(in_channels, 11, len(in_channels)).eval()
        >>> outputs = self.forward(inputs)
        >>> for i in range(len(outputs)):
        ...     print(f'outputs[{i}].shape = {outputs[i].shape}')
        outputs[0].shape = torch.Size([1, 11, 340, 340])
        outputs[1].shape = torch.Size([1, 11, 170, 170])
        outputs[2].shape = torch.Size([1, 11, 84, 84])
        outputs[3].shape = torch.Size([1, 11, 43, 43])
    """

    def __init__(self,
                 in_channels,
                 out_channels,
                 num_outs,
                 start_level=0,
                 end_level=-1,
                 add_extra_convs=False,
                 # extra_convs_on_inputs=True,
                 relu_before_extra_convs=False,
                 no_norm_on_lateral=False,
                 conv_cfg=None,
                 norm_cfg=None,
                 act_cfg=None,
                 upsample_cfg=dict(mode='nearest'),
                 init_cfg=dict(
                     type='Xavier', layer='Conv2d', distribution='uniform')):

        super(FPNDcnLconv3Dcn_afa, self).__init__(init_cfg)
        assert isinstance(in_channels, list)
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.num_ins = len(in_channels)
        self.num_outs = num_outs
        self.relu_before_extra_convs = relu_before_extra_convs
        self.no_norm_on_lateral = no_norm_on_lateral
        self.fp16_enabled = False
        self.upsample_cfg = upsample_cfg.copy()

        if end_level == -1:
            self.backbone_end_level = self.num_ins
            assert num_outs >= self.num_ins - start_level
        else:
            # if end_level < inputs, no extra level is allowed
            self.backbone_end_level = end_level
            assert end_level <= len(in_channels)
            assert num_outs == end_level - start_level
        self.start_level = start_level
        self.end_level = end_level
        self.add_extra_convs = add_extra_convs
        assert isinstance(add_extra_convs, (str, bool))
        if isinstance(add_extra_convs, str):
            # Extra_convs_source choices: 'on_input', 'on_lateral', 'on_output'
            assert add_extra_convs in ('on_input', 'on_lateral', 'on_output')
        elif add_extra_convs:  # True
            self.add_extra_convs = 'on_input'

        self.lateral_convs = nn.ModuleList()
        self.fpn_convs = nn.ModuleList()

        for i in range(self.start_level, self.backbone_end_level):
            '''
            l_conv = ConvModule(
                in_channels[i],
                out_channels,
                3,
                padding=1,
                conv_cfg=conv_cfg,
                norm_cfg=norm_cfg if not self.no_norm_on_lateral else None,
                act_cfg=act_cfg,
                inplace=False)
            '''
            l_conv = DeformConv2dPack(
                in_channels[i],
                out_channels,
                3,
                padding=1
            )
            self.lateral_convs.append(l_conv)
        fpn_conv = DeformConv2dPack(
            (self.backbone_end_level - start_level) * out_channels,
            out_channels,
            3,
            padding=1
        )
        self.fpn_convs.append(fpn_conv)

        # add extra conv layers (e.g., RetinaNet)
        # extra_levels = num_outs - self.backbone_end_level + self.start_level
        # if self.add_extra_convs and extra_levels >= 1:
        #     for i in range(extra_levels):
        #         if i == 0 and self.add_extra_convs == 'on_input':
        #             in_channels = self.in_channels[self.backbone_end_level - 1]
        #         else:
        #             in_channels = out_channels
        #         extra_fpn_conv = ConvModule(
        #             in_channels,
        #             out_channels,
        #             3,
        #             stride=2,
        #             padding=1,
        #             conv_cfg=conv_cfg,
        #             norm_cfg=norm_cfg,
        #             act_cfg=act_cfg,
        #             inplace=False)
        #         self.fpn_convs.append(extra_fpn_conv)



    @auto_fp16()
    def forward(self, inputs):
        """Forward function."""
        assert len(inputs) == len(self.in_channels)

        # build laterals
        laterals = [
            lateral_conv(inputs[i + self.start_level])
            for i, lateral_conv in enumerate(self.lateral_convs)
        ]

        # build top-down path
        used_backbone_levels = len(laterals)
        for i in range(used_backbone_levels - 1, 0, -1):
            # In some cases, fixing `scale factor` (e.g. 2) is preferred, but
            #  it cannot co-exist with `size` in `F.interpolate`.
            if 'scale_factor' in self.upsample_cfg:
                # laterals[i - 1] += F.interpolate(laterals[i],
                #                                 **self.upsample_cfg)
                laterals[i - 1] = torch.cat((laterals[i - 1], F.interpolate(laterals[i], **self.upsample_cfg)), 1)
            else:
                prev_shape = laterals[i - 1].shape[2:]
                # laterals[i - 1] += F.interpolate(
                #    laterals[i], size=prev_shape, **self.upsample_cfg)
                laterals[i - 1] = torch.cat(
                    (laterals[i - 1], F.interpolate(laterals[i], size=prev_shape, **self.upsample_cfg)), 1)

        # build outputs
        # part 1: from original levels

        # if self.training:
        #
        #     outs = [
        #         self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels)
        #     ]
        #     # part 2: add extra levels
        #     if self.num_outs > len(outs):
        #         # use max pool to get more levels on top of outputs
        #         # (e.g., Faster R-CNN, Mask R-CNN)
        #         if not self.add_extra_convs:
        #             for i in range(self.num_outs - used_backbone_levels):
        #                 outs.append(F.max_pool2d(outs[-1], 1, stride=2))
        #         # add conv layers on top of original feature maps (RetinaNet)
        #         else:
        #             if self.add_extra_convs == 'on_input':
        #                 extra_source = inputs[self.backbone_end_level - 1]
        #             elif self.add_extra_convs == 'on_lateral':
        #                 extra_source = laterals[-1]
        #             elif self.add_extra_convs == 'on_output':
        #                 extra_source = outs[-1]
        #             else:
        #                 raise NotImplementedError
        #             outs.append(self.fpn_convs[used_backbone_levels](extra_source))
        #             for i in range(used_backbone_levels + 1, self.num_outs):
        #                 if self.relu_before_extra_convs:
        #                     outs.append(self.fpn_convs[i](F.relu(outs[-1])))
        #                 else:
        #                     outs.append(self.fpn_convs[i](outs[-1]))
        # else:
        outs = [
            self.fpn_convs[0](laterals[0])
        ]

        # outs = [
        #         self.fpn_convs[0](laterals[0])
        #     ]

        return tuple(outs)